Data and code from COVID-19 machine learning paper

Last update: Dec 22, 2022

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Overview

Machine learning approaches for localized lockdown, subnotification analysis and cases forecasting in São Paulo state counties during COVID-19 pandemic.

Sara Malvar, PhD and Julio Romani Meneghini, PhD

Department of Mechanical Engineering, Escola Politécnica, University of São Paulo. Av. Professor Mello Moraes 2231, São Paulo, Brazil.

This repository presents the data, data dictionary and codes used in the analysis of COVID-19 in the State of São Paulo.

DATA: database used in the analysis after cleaning process and data wrangling. This folder also contains the dictionary of variables and their sources.
CLUSTERIZATION: notebook and database of clusterization process.
BENFORD: notebook and databse used for Benford analysis.
LSTM: notebook of neural network for prediction

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Owner

Sara Malvar

PhD in engineering, researcher, mentor and instructor. Machine Learning Research Software Eng. @ Microsoft.

GitHub

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code for paper "Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning" by Zhongzheng Ren, Raymond A. Yeh, Alexander G. Schwing.

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Data and code from COVID-19 machine learning paper

Related tags

Overview

Machine learning approaches for localized lockdown, subnotification analysis and cases forecasting in São Paulo state counties during COVID-19 pandemic.

Sara Malvar, PhD and Julio Romani Meneghini, PhD

You might also like...

Automatic detection and classification of Covid severity degree in LUS (lung ultrasound) scans

Covid19-Forecasting - An interactive website that tracks, models and predicts COVID-19 Cases

COVID-Net Open Source Initiative

Providing the solutions for high-frequency trading (HFT) strategies using data science approaches (Machine Learning) on Full Orderbook Tick Data.

The LaTeX and Python code for generating the paper, experiments' results and visualizations reported in each paper is available (whenever possible) in the paper's directory

Code for paper "Vocabulary Learning via Optimal Transport for Neural Machine Translation"

Code for the paper: Adversarial Machine Learning: Bayesian Perspectives

Reference implementation of code generation projects from Facebook AI Research. General toolkit to apply machine learning to code, from dataset creation to model training and evaluation. Comes with pretrained models.

Codes for realizing theories learned from Data Mining, Machine Learning, Deep Learning without using the present Python packages.

Owner

Sara Malvar

Vowpal Wabbit is a machine learning system which pushes the frontier of machine learning with techniques such as online, hashing, allreduce, reductions, learning2search, active, and interactive learning.

Keeping it safe - AI Based COVID-19 Tracker using Deep Learning and facial recognition

This code reproduces the results of the paper, "Measuring Data Leakage in Machine-Learning Models with Fisher Information"

code for paper "Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning" by Zhongzheng Ren, Raymond A. Yeh, Alexander G. Schwing.

Covid-19 Test AI (Deep Learning - NNs) Software. Accuracy is the %96.5, loss is the 0.09 :)

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A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.

Medical-Image-Triage-and-Classification-System-Based-on-COVID-19-CT-and-X-ray-Scan-Dataset

covid question answering datasets and fine tuned models

Data and code from COVID-19 machine learning paper

Related tags

Overview

Machine learning approaches for localized lockdown, subnotification analysis and cases forecasting in São Paulo state counties during COVID-19 pandemic.

Sara Malvar, PhD and Julio Romani Meneghini, PhD

You might also like...

Automatic detection and classification of Covid severity degree in LUS (lung ultrasound) scans

Covid19-Forecasting - An interactive website that tracks, models and predicts COVID-19 Cases

COVID-Net Open Source Initiative

Providing the solutions for high-frequency trading (HFT) strategies using data science approaches (Machine Learning) on Full Orderbook Tick Data.

The LaTeX and Python code for generating the paper, experiments' results and visualizations reported in each paper is available (whenever possible) in the paper's directory

Code for paper "Vocabulary Learning via Optimal Transport for Neural Machine Translation"

Code for the paper: Adversarial Machine Learning: Bayesian Perspectives

Reference implementation of code generation projects from Facebook AI Research. General toolkit to apply machine learning to code, from dataset creation to model training and evaluation. Comes with pretrained models.

Codes for realizing theories learned from Data Mining, Machine Learning, Deep Learning without using the present Python packages.

Owner

Sara Malvar

Vowpal Wabbit is a machine learning system which pushes the frontier of machine learning with techniques such as online, hashing, allreduce, reductions, learning2search, active, and interactive learning.

Keeping it safe - AI Based COVID-19 Tracker using Deep Learning and facial recognition

This code reproduces the results of the paper, "Measuring Data Leakage in Machine-Learning Models with Fisher Information"

code for paper "Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning" by Zhongzheng Ren*, Raymond A. Yeh*, Alexander G. Schwing.

Covid-19 Test AI (Deep Learning - NNs) Software. Accuracy is the %96.5, loss is the 0.09 :)

Scripts of Machine Learning Algorithms from Scratch. Implementations of machine learning models and algorithms using nothing but NumPy with a focus on accessibility. Aims to cover everything from basic to advance.

This is a Machine Learning Based Hand Detector Project, It Uses Machine Learning Models and Modules Like Mediapipe, Developed By Google!

A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.

Medical-Image-Triage-and-Classification-System-Based-on-COVID-19-CT-and-X-ray-Scan-Dataset

covid question answering datasets and fine tuned models

code for paper "Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning" by Zhongzheng Ren, Raymond A. Yeh, Alexander G. Schwing.